Blockchain and artificial intelligence enabled privacy‐preserving medical data transmission in Internet of Things

Alzubi, Omar A.; Alzubi, Jafar A.; Shankar, K.; Gupta, Deepak

doi:10.1002/ett.4360

Cited by 50 publications

(33 citation statements)

References 24 publications

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“…For tuning the parameters [19][20][21] related to the RBM model, the SFO algorithm is utilized. SFO [22] is a population based metaheuristic approach that is motivated by the attack-alternated method of collection of hunting sailfish that hunt a school of sardine.…”

Section: Parameter Optimization Using Sfo Algorithmmentioning

confidence: 99%

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Ahmed¹,

Althubiti²,

Rao³

et al. 2022

Computers, Materials &Amp; Continua

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Cyberattacks are developing gradually sophisticated, requiring effective intrusion detection systems (IDSs) for monitoring computer resources and creating reports on anomalous or suspicious actions. With the popularity of Internet of Things (IoT) technology, the security of IoT networks is developing a vital problem. Because of the huge number and varied kinds of IoT devices, it can be challenging task for protecting the IoT framework utilizing a typical IDS. The typical IDSs have their restrictions once executed to IoT networks because of resource constraints and complexity. Therefore, this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning (BAIDS-DFPDL) model in IoT Environment. The presented BAIDS-DFPDL model mainly focuses on the identification and classification of intrusions in the IoT environment. To accomplish this, the presented BAIDS-DFPDL model follows blockchain (BC) technology for effective and secure data transmission among the agents. Besides, the presented BAIDS-DFPDL model designs Differential Flower Pollination based feature selection (DFPFS) technique to elect features. Finally, sailfish optimization (SFO) with Restricted Boltzmann Machine (RBM) model is applied for effectual recognition of intrusions. The simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

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Section: Parameter Optimization Using Sfo Algorithmmentioning

confidence: 99%

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Ahmed¹,

Althubiti²,

Rao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A conventional blockchain employed is cryptocurrency, where the user requires a blockchain that handles secure event messages with no crypto coins. Consequently, it applies security event messages [23][24][25][26][27].…”

Section: Blockchain Assisted Vehicular Communicationmentioning

confidence: 99%

Blockchain Driven Metaheuristic Route Planning in Secure Vehicular Adhoc Networks

Hassine¹,

Alotaibi²,

Alsolai³

et al. 2022

Computers, Materials &Amp; Continua

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Nowadays, vehicular ad hoc networks (VANET) turn out to be a core portion of intelligent transportation systems (ITSs), that mainly focus on achieving continual Internet connectivity amongst vehicles on the road. The VANET was utilized to enhance driving safety and build an ITS in modern cities. Driving safety is a main portion of VANET, the privacy and security of these messages should be protected. In this aspect, this article presents a blockchain with sunflower optimization enabled route planning scheme (BCSFO-RPS) for secure VANET. The presented BCSFO-RPS model focuses on the identification of routes in such a way that vehicular communication is secure. In addition, the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of routes. Besides, the proposed BCSFO-RPS model derives an intrusion detection system (IDS) encompassing two processes namely feature selection and classification. To detect intrusions, correlation based feature selection (CFS) and kernel extreme machine learning (KELM) classifier is applied. The performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.

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“…AI is found to be broadly benefitting modern medicine and clinical practice [ 2 ]. A range of AI-based applications, such as clinical and genomic diagnostics [ 3 , 4 ], chronic disease management [ 5 ], blockchain technology [ 6 ] and sophisticated surgical robots [ 7 ] have deeply reshaped medical practice in the 21st century. As an emerging field, AI will only continue to transform the way medical science advances and become an integral part of medicine in the future [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

Jiang

Jong

et al. 2022

IJERPH

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Medical students learning to use artificial intelligence for medical practices is likely to enhance medical services. However, studies in this area have been lacking. The present study investigated medical students’ perceptions of and behavioral intentions toward learning artificial intelligence (AI) in clinical practice based on the theory of planned behavior (TPB). A sum of 274 Year-5 undergraduates and master’s and doctoral postgraduates participated in the online survey. Six constructs were measured, including (1) personal relevance (PR) of medical AI, (2) subjective norm (SN) related to learning medical AI, (3) perceived self-efficacy (PSE) of learning medical AI, (4) basic knowledge (BKn) of medical AI, (5) behavioral intention (BI) toward learning medical AI and (6) actual learning (AL) of medical AI. Confirmatory factor analysis and structural equation modelling were employed to analyze the data. The results showed that the proposed model had a good model fit and the theoretical hypotheses in relation to the TPB were mostly confirmed. Specifically, (a) BI had a significantly strong and positive impact on AL; (b) BI was significantly predicted by PR, SN and PSE, whilst BKn did not have a direct effect on BI; (c) PR was significantly and positively predicted by SN and PSE, but BKn failed to predict PR; (d) both SN and BKn had significant and positive impact on PSE, and BKn had a significantly positive effect on SN. Discussion was conducted regarding the proposed model, and new insights were provided for researchers and practitioners in medical education.

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Blockchain and artificial intelligence enabled privacy‐preserving medical data transmission in Internet of Things

Cited by 50 publications

References 24 publications

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Blockchain Driven Metaheuristic Route Planning in Secure Vehicular Adhoc Networks

Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

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